Observatorio de I+D+i UPM

Memorias de investigación
Ponencias en congresos:
Efficient Monte Carlo Optimization for Multi-Label Classifier Chains
Año:2013
Áreas de investigación
  • Análisis multivariante,
  • Inferencia no paramétrica,
  • Inteligencia artificial (redes neuronales, lógica borrosa, sistemas expertos, etc),
  • Reconocimiento de patrones
Datos
Descripción
Multi-label classification (MLC) is the supervised learning problem where an instance may be associated with multiple labels. Modeling dependencies between labels allows MLC methods to improve their performance at the expense of an increased computational cost. In this paper we focus on the classifier chains (CC) approach for modeling dependencies. On the one hand, the original CC algorithm makes a greedy approximation, and is fast but tends to propagate errors down the chain. On the other hand, a recent Bayes-optimal method improves the performance, but is computationally intractable in practice. Here we present a novel double-Monte Carlo scheme (M2CC), both for finding a good chain sequence and performing efficient inference. The M2CC algorithm remains tractable for high-dimensional data sets and obtains the best overall accuracy, as shown on several real data sets with input dimension as high as 1449 and up to 103 labels.
Internacional
Si
Nombre congreso
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Tipo de participación
960
Lugar del congreso
Vancouver (Canadá)
Revisores
Si
ISBN o ISSN
978-1-4799-0356-6
DOI
Fecha inicio congreso
26/05/2013
Fecha fin congreso
31/05/2013
Desde la página
3457
Hasta la página
3461
Título de las actas
Proceedings of the 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: David Luengo Garcia (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Departamento: Ingeniería de Circuitos y Sistemas
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